A continuous wavelet transform approach for harmonic parameters estimation in the presence of impulsive noise

Dai, Yu; Xue, Yuan; Zhang, Jianxun

doi:10.1016/j.jsv.2015.09.023

Cited by 14 publications

(4 citation statements)

References 31 publications

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“…Among all mother wavelets, the selected wavelet is the complex Morlet wavelet (CMW) and the results showed that it has satisfying results regarding bearing and gear fault detection. Lately, author proposed in Dai et al (2016) a continuous wavelet transform approach for effective harmonic parameters estimation within the detection and elimination of impulsive noise. In the context of PHM, recently, CWT was joined to a blind source separation technique to analyze the wavelet coefficients and the evolution of each independent source is used for health assessment (Benkedjouh et al, 2018).…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes

Soualhi¹,

Hawwari

Medjaher

et al. 2020

IJPHM

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The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes.

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Section: Continuous Wavelet Transformmentioning

confidence: 99%

PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes

Soualhi¹,

Hawwari

Medjaher

et al. 2020

IJPHM

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“…The research of detection has become a problem that needs to be solved urgently in the current power system. Prior to this, a large number of scholars have carried out a lot of research on the detection and elimination of harmonics in power systems, and have proposed many advanced research theories and solutions, including Fast Fourier Transform [3][4][5] (FFT), wavelet transform [6][7] (WT), instantaneous reactive power [8] and other methods and related improved methods. At present, most of the commonly used algorithms are improved algorithms based on fast Fourier transform or wavelet transform combined with other methods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive harmonic separation and detection algorithm based on deep neural network

Wang¹,

Zhang²

2022

5th International Conference on Computer Information Science and Application Technology (CISAT 2022)

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“…Accini et al proposed a novel algorithm to detect position of the drilling bit and realize the control of bone drilling only based on a position sensor [ 16 ]. Dai et al pointed out that bone status can be monitored by analyzing the vibration signals of bones and presented a non-contact system to collect and analyze the vibration signals by using a laser displacement sensor, and eventually the system achieved real-time detection of the drilling state in thoracic surgery, overcoming the shortcomings of any kinds of contact sensor [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

State Recognition of Bone Drilling Based on Acoustic Emission in Pedicle Screw Operation

Guan

Sun

et al. 2018

Sensors

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Pedicle drilling is an important step in pedicle screw fixation and the most significant challenge in this operation is how to determine a key point in the transition region between cancellous and inner cortical bone. The purpose of this paper is to find a method to achieve the recognition for the key point. After acquiring acoustic emission (AE) signals during the drilling process, this paper proposed a novel frequency distribution-based algorithm (FDB) to analyze the AE signals in the frequency domain after certain processes. Then we select a specific frequency domain of the signal for standard operations and choose a fitting function to fit the obtained sequence. Characters of the fitting function are extracted as outputs for identification of different bone layers. The results, which are obtained by detecting force signal and direct measurement, are given in the paper. Compared with the results above, the results obtained by AE signals are distinguishable for different bone layers and are more accurate and precise. The results of the algorithm are trained and identified by a neural network and the recognition rate reaches 84.2%. The proposed method is proved to be efficient and can be used for bone layer identification in pedicle screw fixation.

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A continuous wavelet transform approach for harmonic parameters estimation in the presence of impulsive noise

Cited by 14 publications

References 31 publications

PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes

PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes

Adaptive harmonic separation and detection algorithm based on deep neural network

State Recognition of Bone Drilling Based on Acoustic Emission in Pedicle Screw Operation

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